Capturing User Generated Video Content in Online Social Networks

  • Clinton Daniel
  • Matthew Mullarkey
  • Alan R. Hevner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10844)


We build and evaluate an innovative artifact for the investigation of social content derived-platforms specifically to gain a unique understanding of the content shared and underlying behaviors of the contributors to these technology platforms. The artifact‘s innovation is derived from the solution’s unique approach to converting and analyzing the multimedia – especially video - content to gain interesting insights into the social network connectivity of the actors on a given technology platform. The artifact directly addresses a practical need for industry practitioners to analyze social video network content using a rigorous and evidence-based DSR approach.


User video content Social network technology platforms DSR Elaborated ADR 


  1. 1.
    Abhari, A., Soraya, M.: Workload generation for YouTube. Multimed. Tools Appl. 46, 91–118 (2009). Scholar
  2. 2.
    Ahmad, U., Zahid, A., Shoaib, M., AlAmri, A.: HarVis: an integrated social media content analysis framework for YouTube platform. Inf. Syst. 69, 25–39 (2017)CrossRefGoogle Scholar
  3. 3.
    Arnold, C.W., Oh, A., Chen, S., Speier, W.: Evaluating topic model interpretability from a primary care physician perspective. Comput. Methods Program. Biomed. 24, 67–75 (2016)CrossRefGoogle Scholar
  4. 4.
    Blei, D., Ng, A., Jordan, M.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  5. 5.
    Borgatti, S.P., Everett, M.G., Johnson, J.C.: Analyzing Social Networks. SAGE, Los Angeles (2013)Google Scholar
  6. 6.
    Cha, M., Kwak, H., Rodriguez, P., Moon, S.: I tube, you tube, everybody tubes: analyzing the world’s largest user generated content video system. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, pp. 1–14. ACM, San Diego (2007)Google Scholar
  7. 7.
    Chang, J., Boyd-Graber, J., Gerrish, S., Wang, C., Blei, D.M.: Reading tea leaves: how humans interpret topic models. In: Proceedings of the 22nd International Conference on Neural Information Processing Systems, NIPS 2009, pp. 288–296. Proceedings of the 2009 Conference, Vancouver (2009)Google Scholar
  8. 8.
    Chatzopoulou, G., Sheng, C., Faloutsos, M.: A first step towards understanding popularity in YouTube. In: INFOCOMM IEEE Conference on Computer Communications Workshops, pp. 1–6. IEEE, San Diego (2010).
  9. 9.
    Chen, L.-C., Tesng, H.-H., Liao, I.-E.: Information and communication technology trend analysis using. In: Recent Researches in Applied Informatics: Proceedings of the 6th International Conference on Applied Informatics and Computing Theory (AICT 2015), pp. 158–166. WSEAS Press, Salerno (2015)Google Scholar
  10. 10.
    Cheng, X., Dale, C., Liu, J.: Statistics and social network of youtube videos. In: 16th International Workshop, IQQos 2008, pp. 229–238. IEEE, Enschede (2008)Google Scholar
  11. 11.
    Daniel, C., Dutta, K.: Automated generation of latent topics on emerging technologies from YouTube Video content. In: Proceedings of the 51st Hawaii International Conference on System Sciences 2018, pp. 1762–1770 (2018).
  12. 12.
    Figueiredo, F., Benevenuto, F., Almeida, J.: The tube over time: characterizing popularity growth of youtube videos. Retrieved from Fabricio Benevenuto, Computer Science Department, Federal University of Minas Gerais (2011).
  13. 13.
    Gill, P., Arlitt, M., Li, Z., Mahanti, A.: Youtube traffic characterization: a view from the edge. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, pp. 15–28. ACM, Dan Diego (2007)Google Scholar
  14. 14.
    Gupta, H., Singh, S., Sinha, P.: Multimedia tool as a predictor for social media advertising- a YouTube way. Multimed. Tools Appl. 76(18), 18557–18568 (2017)CrossRefGoogle Scholar
  15. 15.
    Harrison, D., Wilding, J., Bowman, A., Fuller, A., Nicholls, S.G., Pound, C.M., Sampson, M.: Using YouTube to disseminate effective vaccination pain treatment for babies. PLoS ONE 11(10), 1–10 (2016)Google Scholar
  16. 16.
    Hevner, A.R.: Design science research. In: Tucker, A., Topi, H. (eds.) Computing Handbook, 3rd edn, pp. 22-1–22-23. Chapman and Hall/CRC, New York (2014)Google Scholar
  17. 17.
    Hevner, A.R., March, S.T., Park, J., Ram, S.: Design science in information systems. MIS Q. 28(1), 75–105 (2004)CrossRefGoogle Scholar
  18. 18.
    Hevner, A., Chatterjee, S.: Design research in information systems. Springer, New York (2010). Eds. by R. Sharda and S. VobCrossRefGoogle Scholar
  19. 19.
    Lange, P.G.: Publicly private and privately public: social networking on YouTube. J. Comput. Mediat. Commun. 13, 361–380 (2008)CrossRefGoogle Scholar
  20. 20.
    Malik, H., Tian, Z.: A Framework for collecting YouTube meta-data. In: The 8th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2017), vol. 113, pp. 194–201. Procedia Computer Science (2017)Google Scholar
  21. 21.
    Miller, E.D.: Content analysis of select YouTube postings: comparisons of reactions to the sandy hook and aurora shootings and hurricant sandy. Cyberpsychol. Behav. Soc. Netw. 18(11), 635–640 (2015)CrossRefGoogle Scholar
  22. 22.
    Mullarkey, M.T., Hevner, A.R.: Entering action design research. In: Donnellan, B., Helfert, M., Kenneally, J., VanderMeer, D., Rothenberger, M., Winter, R. (eds.) DESRIST 2015. LNCS, vol. 9073, pp. 121–134. Springer, Cham (2015). Scholar
  23. 23.
    Mullarkey, M.T., Hevner, A.R.: An elaborated action design research process model. Eur. J. Inf. Syst. (2018).
  24. 24.
    Peffers, K., Tuunanen, T., Rothenberger, M., Chatterjee, S.: A design science research methodology for information systems research. J. MIS 24(3), 45–77 (2008)Google Scholar
  25. 25.
    Robins, G.: Doing Social Network Research. SAGE, Los Angeles (2015)Google Scholar
  26. 26.
    Santos, R., Rocha, B., Rezende, C.G., Loureiro, A.: Characterizing the YouTube video-sharing community. Retrieved from Rodrygo Santos: Department of Computer Science, Federal University of Minas Gerais (2007).
  27. 27.
    Sein, M.K., Henfridsson, O., Purao, S., Rossi, M., Lindgren, R.: Action design research. MIS Q. 35(1), 37–56 (2011)CrossRefGoogle Scholar
  28. 28.
    Siersdorfer, S., Nejdl, W., Chelaru, S., San Pedro, J.: How useful are your comments?: analyzing and predicting youtube comments and comment ratings. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 891–900. ACM, Raleigh (2010)Google Scholar
  29. 29.
    Smith, C., Allman, T., Crocker, S.: Reading between the lines: accessing information via YouTube’s automatic captioning. Online Learn. 21(1), 115–131 (2017)Google Scholar
  30. 30.
    Yoganarasimhan, H.: Impact of social network structure on content propagation: a study using YouTube data. Quant. Mark. Econ. 10, 111–150 (2009). Scholar
  31. 31.
    Yuan, J., Zheng-Jun, Z., Zheng, Y.-T., Wang, M., Zhou, X., Chua, T.-S.: Utilizing related samples to enhance interactive concept-based video search. IEEE Trans. Multimed. 13(6), 1343–1355 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Clinton Daniel
    • 1
  • Matthew Mullarkey
    • 1
  • Alan R. Hevner
    • 1
  1. 1.Department of Information Systems Decision SciencesUniversity of South FloridaTampaUSA

Personalised recommendations